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Crude oil price prediction based on a dynamic correcting support vector regression machine. (English) Zbl 1273.91190

Summary: A new accurate method on predicting crude oil price is presented, which is based on \(\varepsilon\)-support vector regression (\(\varepsilon\)-SVR) machine with dynamic correction factor correcting forecasting errors. We also propose the hybrid RNA genetic algorithm (HRGA) with the position displacement idea of bare bones particle swarm optimization (PSO) changing the mutation operator. The validity of the algorithm is tested by using three benchmark functions. From the comparison of the results obtained by using HRGA and standard RNA genetic algorithm (RGA), respectively, the accuracy of HRGA is much better than that of RGA. In the end, to make the forecasting result more accurate, the HRGA is applied to the optimize parameters of \(\varepsilon\)-SVR. The predicting result is very good. The method proposed in this paper can be easily used to predict crude oil price in our life.

MSC:

91B24 Microeconomic theory (price theory and economic markets)
68T05 Learning and adaptive systems in artificial intelligence
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